In-Situ Processing in Climate Science

Röber, Niklas; Engels, Jan Frederik

doi:10.1007/978-3-030-34356-9_46

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Large Data Visualization1

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2020

2021

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Cited by 3 publications

(1 citation statement)

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“…This has the advantage that both the I/O and the time to solution are drastically reduced. In our experience, developing and experimenting with an in situ adaptor for our Fortran ICON model based on ParaView/Catalyst, and running it together with the simulation in a tightly coupled setup, the performance overhead required for the processing and visualization is almost negligible 10 . At the moment, there are a lot of ongoing developments and community efforts aiming in the same direction.…”

Section: Large Data Visualizationmentioning

confidence: 99%

Visualization of Climate Science Simulation Data

Röber

Böttinger

Stevens

et al. 2021

IEEE Comput. Grap. Appl.

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Section: Large Data Visualizationmentioning

confidence: 99%

Visualization of Climate Science Simulation Data

Röber

Böttinger

Stevens

et al. 2021

IEEE Comput. Grap. Appl.

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Dynamic 3-D Visualization of Climate Model Development and Results

Walton

Adams

Hayek

et al. 2021

IEEE Comput. Grap. Appl.

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State of the Art and Future Trends in Data Reduction for High-Performance Computing

2020

JSFI

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Research into data reduction techniques has gained popularity in recent years as storage capacity and performance become a growing concern. This survey paper provides an overview of leveraging points found in high-performance computing (HPC) systems and suitable mechanisms to reduce data volumes. We present the underlying theories and their application throughout the HPC stack and also discuss related hardware acceleration and reduction approaches. After introducing relevant use-cases, an overview of modern lossless and lossy compression algorithms and their respective usage at the application and file system layer is given. In anticipation of their increasing relevance for adaptive and in situ approaches, dimensionality reduction techniques are summarized with a focus on non-linear feature extraction. Adaptive approaches and in situ compression algorithms and frameworks follow. The key stages and new opportunities to deduplication are covered next. An unconventional but promising method is recomputation, which is proposed at last. We conclude the survey with an outlook on future developments.

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In-Situ Processing in Climate Science

Cited by 3 publications

References 18 publications

Visualization of Climate Science Simulation Data

Visualization of Climate Science Simulation Data

Dynamic 3-D Visualization of Climate Model Development and Results

State of the Art and Future Trends in Data Reduction for High-Performance Computing

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